Presently, with the rapid development of economy and society, power distribution reliability, power quality, carbon emission and energy efficiency of power system are facing unprecedented strict constraints, traditional power grid is transforming into Smart Grid. (Refer to: Yu Yinxin, Luan Wenpeng. The smart grid of the 21st century. [EB/OL]. http: //news. sciencenet.cn. 2010-09-05). Wherein, the Advanced Metering Infrastructure (AMI) is the very first step for realizing power grid intellectualization, which bears the underlying task of the measurement, collection, storage and analysis of multi-aspect electrical information for the entire electrical grid, and is the foundation of the implementation of other (upper) smart grid functions, such as power grid fault location and self-healing, demand side management and real-time load balancing, voltage stability control, accurate load forecasting and modeling, real-time power system simulation analysis, etc. Electricity power load monitoring and analysis is one of the most important parts of advanced metering system, wherein the significance of residential electricity monitoring is that: on one hand, the customers may understand the details of the power consumption and prompt their power consumption habits, and eventually achieve cutting the electricity bills; on the other hand, since residential load has strong controllability and great potential of friendly cooperation with the power grid, for instance, energy storage type load such as electric water heater and air conditioner, and delayed type load such as washing machine and electric kettle, customers can respond to the demand of power grid accordingly via electricity monitoring, which helps to achieve the functions of demand side management such as “peak load shifting”, and eventually benefits the grid operation.
Traditional residential electricity monitoring equips every appliance with one sensor to track its power state, i.e. electric power, and operating state (for instance, air-conditioner has two operating state of cooling and heating with different power.), which belongs to “intrusive” load monitoring solution. However, the installation, debugging and maintenance of a large number of sensors with digital communication need large expenditure, moreover, too many sensors would lower the reliability of the monitoring system and the appliances as well. George Hart first formally present Non-Intrusive Load Monitoring System in his paper of “G. W. Hart. Nonintrusive appliance load monitoring [J]. Proceedings of IEEE, 1992, 80(12): 1870-1891”, the basic principles can be summarized as shown in FIG. 1. The main functional blocks of the system are enclosed in the dashed box of FIG. 1, wherein, the data acquisition module is for sampling the terminal voltage and total current at the supply access, the data preprocessing module is for filtering and denoising voltage and current waveform and operating other operations for load electrical feature extraction, such as harmonic analysis of voltage and current signal, phase correction for the current waveform and so on; and the load event detection and feature extraction module is for extracting the required load signatures of the monitoring method from the measured terminal voltage and total current on the basis of load event detection, such as steady-state real power and reactive power, transient current peak-value, transient current RMS value and so on, and this module has a critical impact on the monitoring performance. Finally effective monitoring method is utilized to complete the total load composition analysis and electrical power state identification, and to achieve maintenance and management functions of the monitoring system. In addition, some other extending functions the system may have are also shown in the figure, such as interactive operation, control command input and output, system reporting and so on.
Non-intrusive power load monitoring scheme analyzes the voltage and total current sampled from the access of user power supply to determine the status of each electric appliances indoor. This scheme can not only reduce the monitoring cost and simplify operation, but also improve the reliability of the monitoring system, moreover, it can simplify the work of collecting detail data of power consumption for utilities, and instruct the customers to optimize the power consumption simply and easily as well.
In the prior art, transforming the load operating states, such as start-up and shut-down, are treated as load event, event-related steady-state features (for instance, the steady-state power step, refer to: (1) G. W. Hart. Nonintrusive appliance changes start, stop and other working conditions collectively load monitoring[J]. Proceedings of IEEE, 1992, 80(12): 1870-1891; (2) H. Pihala. Non-intrusive appliance load monitoring system based on a modern kWh-meter[R]. Technical Research Center of Finland, ESPOO, 1998; (3) Christopher E. Reeg, Thomas J. Overbye. Algorithm development for Non-Intrusive Load Monitoring for Verification and Diagnostics [C]. North American Power Symposium (NAPS), 2010, :1-5; ((4) Berges, et al. Enhancing electricity audits in residential buildings with nonintrusive load monitoring[J]. Journal of Industrial Ecology, 2010, 14(5):844-858; (5) Ming Dong, et al. An Event Window Based Load Monitoring Technique for Smart Meters[J]. IEEE Transactions on Smart Grid, 2012, 3(2):787-796) or transient features (for instance, transient spectral envelope pattern, refer to: (1) Steven B. Leeb, James. L. Kirtley. A Multiscale Transient Event Detector for Nonintrusive Load Monitoring[J]. International Conference on Industrial Electronics, Control, and Instrumentation, 1993, 1:354-359; (2) Steven B. Leeb, Steven R. Shaw, James L. Kirtley. Transient event detection in spectral envelope estimates for nonintrusive load monitoring [J]. IEEE Transactions on Power Delivery, 1995, 10 (3):1200-1210; (3) S. R. Shaw, et al. Nonintrusive Load Monitoring and Diagnostics in Power Systems[J]. IEEE Transactions on Instrumentation and Measurement, 2008, 57(7): 1445-1454) are used to identify which appliance of the total load generating the detected load event, and further to realize non-intrusive load monitoring. Among existing achievement, based on the works of “G. W. Hart. Nonintrusive 15 appliance loadmonitoring [J]. Proceedings of IEEE, 1992, 80(12): 1870-1891”, “Christopher E. Reeg, Thomas J. Overbye. Algorithm development for Non-Intrusive Load Monitoring for Verification and Diagnostics[C]. North American Power Symposium (NAPS), 2010, :1-5” improved the monitoring performance via considering the feature of typical operating time period. Ming Dong, et al. “An Event Window Based Load Monitoring Technique for Smart Meters[J]. IEEE Transactions on Smart Grid, 2012, 3(2):787-796” proposed the concept of load event window, and on the basis of load characteristics parameterization, provided a linear load recognition classifier in the form of parameter equation, which has a high detection accuracy. However, determining the constant parameters of the classifier equations requires statistical analyzing a large number of actual data measured on site, inappropriate values would greatly affect the recognition accuracy. S. R. Shaw, et al. “Nonintrusive Load Monitoring and Diagnostics in Power Systems[J]. IEEE Transactions on Instrumentation and Measurement, 2008, 57(7): 1445-1454” realized the parameterization of transient power spectral pattern, which can be used to identify those individual appliances belonging to the same class, while with different transient waveform parameters. In summary, as to such event-based load monitoring methods, due to switching the operating states of different appliances are not often independently, different load events may happen simultaneously or sequentially to cause the unique characteristics of individual appliance to be hidden or just disappear, and thus resulting in incorrect identification results. For short duty-cycle appliance, the loss of load events between two consecutive measurement points may produce its identification errors. In addition, event-based methods cannot identify always-on appliances, and has lower monitoring accuracy for appliances with multiple operating states and with continuously variable power. In the reference of Li Peng, Yu Yinxin. “Nonintrusive Method for On-Line Power Load Decomposition[J]. Journal of Tianjin University, 2009, 42(4):303-308.”, it puts aside the concepts of load events, based on the fact that any steady-state total load current can be approximately estimated by a linear superposition of the steady-state currents of the major types of electrical equipment inside the load, and uses optimization method to achieve the optimal matching of the actual total load current pattern (current harmonic-characteristics) with the estimated one, and thus obtaining current weight coefficients of various types of electrical equipment, and realizing on-line disaggregation of the total power. The proposed method effectively solves the above-described problems existing in the load event-based methods. Nevertheless, if the current waveforms of different appliances are similar, the total power disaggregation accuracy will decrease, thus proper appliance classification is required in advance, and the proposed method fails to satisfactorily resolve the problem of identification of appliance operating state. Literature “(1) Yi-Sheng Lai, Yung-Chi Chen, Shiao-Li Tsao, Tzung-Cheng Tsai. A novel search scheme for non-intrusive load monitoring systems[C]. 2012 IEEE International Conference on Industrial Technology (ICIT), 2012: 102-107” and literature “(2) Jian Liang, Ng, S., Kendall, G., Cheng, J. Load Signature Study—Part II_Disaggregation Framework, Simulation, and Applications [J]. IEEE Transactions on Power Delivery, 2010, 25(2): 561-569” are all relate to comprehensive non-intrusive load monitoring method integrating various load characteristics generated from transient process and steady-state operation, generally this kind of method provides higher identification and monitoring accuracy. However, higher sampling frequency (e.g. involving transient characteristics) of the data acquisition module and higher performance requirements of microprocessors would increase the overall cost and reduce the practicability of the monitoring methods.